Multiple Penalties Research Articles

Overlapping Penalties in the Jurisprudence of the Four Schools of Thought: A Comparative Jurisprudential Study

The study of overlapping crimes and their punishment in Islamic jurisprudence deals with the issue of one person committing more than one crime, either at one time or in successive and different times. This crime may be of one gender, such as murder or theft, and a person may commit multiple crimes, such as stealing and drinking alcohol. This research looked at the opinions of the jurists in the penalties of such crimes. The research concluded that the jurists unanimously agreed that the criminal should not be punished if he committed several crimes of the same sex, except for one punishment only. For example, if the unmarried person commits the crimes of adultery and defamation, then one punishment is imposed on him, which is the punishment for adultery. The penalty of slander is included in it. If adultery is punished and then slandered after that, then the penalty of slander is applied to it as well. Overlapping means that the penalty for the lesser crime is included in the penalty for the major crime if they are committed together. In studying this subject, I followed the descriptive, analytical and comparative approach, where I explained the concept of overlap, its location, causes, and wording, the definition of punishment and its types, its wisdom and conditions, the penalties sections, the combination of crimes of one sex, the combination of crimes of different races, and how to fulfill multiple penalties, the principle Applications of multiple penalties with a statement of the sayings of the four schools of thought in each issue and a statement of the most correct of them.

On the maximum penalized full likelihood approach for Cox model with extreme value for heavily censored survival data

In this paper, to deal with heavily censored survival data, a penalized full likelihood (PFL) with extreme value is proposed to estimate the regression coefficients and baseline hazard function in Cox model simultaneously, where multiple penalties are used for variable selection. We present a single-loop algorithm to fit the tail of the baseline distribution beyond a threshold with an extreme value model. The proposed maximum PFL estimators are proved to possess good asymptotic properties, which are validated by simulations and real data analysis.

The role of the public prosecutors in the repression of tax crimes - the Italian perspective

The investigation and prosecution of tax crimes is characterized by the coexistence of administrative and criminal proceedings, which can lead to the imposition of multiple penalties. This paper therefore analyses the role of the public prosecutor’s office in the prevention and prosecution of tax crimes.

Multiple Penalties and Multiple Local Surrogates for Expensive Constrained Optimization

This article proposes an evolutionary algorithm using multiple penalties and multiple local surrogates (MPMLS) for expensive constrained optimization. In each generation, MPMLS defines and optimizes a number of subproblems. Each subproblem penalizes the constraints in the original problem using a different penalty coefficient and has its own search subregion. A local surrogate is built for optimizing each subproblem. Two major advantages of MPMLS are: 1) it can maintain good population diversity so that the search can approach the optimal solution of the original problem from different directions and 2) it only needs to build local surrogates so that the computational overhead of the model building can be reduced. Numerical experiments demonstrate that our proposed algorithm performs much better than some other state-of-the-art evolutionary algorithms.

Continuous-Domain Formulation of Inverse Problems for Composite Sparse-Plus-Smooth Signals

We present a novel framework for the reconstruction of 1D composite signals assumed to be a mixture of two additive components, one sparse and the other smooth, given a finite number of linear measurements. We formulate the reconstruction problem as a continuous-domain regularized inverse problem with multiple penalties. We prove that these penalties induce reconstructed signals that indeed take the desired form of the sum of a sparse and a smooth component. We then discretize this problem using Riesz bases, which yields a discrete problem that can be solved by standard algorithms. Our discretization is exact in the sense that we are solving the continuous-domain problem over the search space specified by our bases without any discretization error. We propose a complete algorithmic pipeline and demonstrate its feasibility on simulated data.

Regularized bridge-type estimation with multiple penalties

The aim of this paper is to introduce an adaptive penalized estimator for identifying the true reduced parametric model under the sparsity assumption. In particular, we deal with the framework where the unpenalized estimator of the structural parameters needs simultaneously multiple rates of convergence (i.e., the so-called mixed-rates asymptotic behavior). We introduce a bridge-type estimator by taking into account penalty functions involving $$\ell ^q$$ norms (0 < q ≤ 1). We prove that the proposed regularized estimator satisfies the oracle properties. Our approach is useful for the estimation of stochastic differential equations in the parametric sparse setting. More precisely, under the high-frequency observation scheme, we apply our methodology to an ergodic diffusion and introduce a procedure for the selection of the tuning parameters. Furthermore, the paper contains a simulation study as well as a real data prediction in order to assess about the performance of the proposed bridge estimator.

PROSPEK QANUN ACEH NO.6 TAHUN 2014 TENTANG UQUBAT TA’ZIR JARIMAH PEMERKOSAAN

Regarding the prospect of Aceh’s Qanun Jinayat No. 6 Year 2014 about uqubat ta’zir jarimah of rape, there should be a cooperation between stakeholders and institution concerned in succeeding Aceh’s Qanun Jinayat, and the revision of uqubat ta’zir of rape by using a single system, in the form of the word “and” not the word “or” that is contained in article 48, 49, 50, to make the offenders obtain the multiple penalties, because the jarimah of rape must be severely punished. And for uqubat ta’zir jarimah of rape in Aceh’s Qanun Jinayat, if it is investigated further, there is no difference. In the present many academics and practitioners recommend that the rape penalty within criminal code (KUHP) should be revised because the punishment is considered ineffective to reduce and even solve the cases of rape in Indonesia.

Contour recognition of roadheader cutting head based on shape matching

In this paper, a shape matching algorithm is presented to perform the cutting head recognition for roadheaders based on a binocular vision system installed on the machine. The shape descriptor is a simplified intersection angle of tangent lines which is firstly proposed on the basis of contour points’ spatial positions. It exhibits significant self-contained property and features describing capacity for both partial and whole shapes. To achieve the best match, the MVM algorithm was improved with skipping and multiple matching penalties to complete the multiple mapping and to skip existing outliers in sequences. More precisely, these advantages make the proposed algorithm less sensible to the local variations caused by varied views. Experimental results demonstrated that the proposed method outperforms existing ones with stronger robustness and higher accuracy. In actual pictures tests, the cutting head recognition rate reached 100% with spatial positioning errors under 2.2 cm, which met the requirements for accurate location in real time.

A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests.

The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within a pathway). Testing sets of variants such as pathways for association with a disease phenotype reduces multiple testing penalties, may increase power, and allows for straightforward biological interpretation. However, a significant variant-set association test does not indicate precisely which variants contained within that set are causal. Because pathways often contain many variants, it may be helpful to follow-up significant pathway tests by conducting gene-based tests on each gene in that pathway to narrow in on the region of causal variants. In this paper, we propose such a multistep approach for variant-set analysis that can also account for covariates and complex pedigree structure. We demonstrate this approach on simulated phenotypes from Genetic Analysis Workshop 19. We find generally better power for the multistep approach when compared to a more conventional, single-step approach that simply runs gene-based tests of association on each gene across the genome. Further work is necessary to evaluate the multistep approach on different data sets with different characteristics.

MO-FG-CAMPUS-IeP2-04: Multiple Penalties with Different Orders for Structure Adaptive CBCT Reconstruction

Purpose: To combine total variation (TV) and Hessian penalty in a structure adaptive way for cone-beam CT (CBCT) reconstruction. Methods: TV is a widely used first order penalty with good ability in suppressing noise and preserving edges but leads to the staircase effect in regions with smooth intensity transition. The second order Hessian penalty can effectively suppress the staircase effect with extra cost of blurring object edges. To take the best of both penalties we proposed a novel method to combine both for CBCT reconstruction in a structure adaptive way. The proposed method adaptively determined the weight of each penalty according to the geometry of local regions. An specially-designed exponent term with image gradient involved was used to characterize the local geometry such that the weights for Hessian and TV were 1 and 0 respectively at uniform local regions and 0 and 1 at edge regions. For other local regions the weights varied from 0 to 1. The objective functional was minimized using the majorzationminimization approach. We evaluated the proposed method on a modified 3D shepp-logan and a CatPhan 600 phantom. The full-width-at-halfmaximum (FWHM) and contrast-to-noise (CNR) were calculated. Results: For 3D shepp-logan the reconstructed images using TV had an obvious staircase effect while those using the proposed method and Hessian preserved the smooth transition regions well. FWHMs of the proposed method TV and Hessian penalty were 1.75 1.61 and 3.16 respectively, indicating that both TV and the proposed method is able to preserve edges. For CatPhan 600 CNR values of the proposed method were similar to those of TV and Hessian. Conclusion: The proposed method retains favorable properties of TV like preserving edges and also has the ability in better preserving gradual transition structure as Hessian does. All methods performs similarly in suppressing noise. This work was supported in part by National Natural Science Foundation of China (NNSFC) under Grant Nos.60971112 and 61375018 grants from the Cancer Prevention and Research Institute of Texas (RP130109 and RP110562-P2) National Institute of Biomedical Imaging and Bioengineering (R01 EB020366) and a grant from the American Cancer Society (RSG-13-326-01-CCE).

Multi-parameter Tikhonov regularization — An augmented approach

We study multi-parameter regularization (multiple penalties) for solving linear inverse problems to promote simultaneously distinct features of the sought-for objects. We revisit a balancing principle for choosing regularization parameters from the viewpoint of augmented Tikhonov regularization, and derive a new parameter choice strategy called the balanced discrepancy principle. A priori and a posteriori error estimates are provided to theoretically justify the principles, and numerical algorithms for efficiently implementing the principles are also provided. Numerical results on deblurring are presented to illustrate the feasibility of the balanced discrepancy principle.

EEG source imaging with spatio‐temporal tomographic nonnegative independent component analysis

This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.

Modelling and Smoothing Parameter Estimation With Multiple Quadratic Penalties

Summary Penalized likelihood methods provide a range of practical modelling tools, including spline smoothing, generalized additive models and variants of ridge regression. Selecting the correct weights for penalties is a critical part of using these methods and in the single-penalty case the analyst has several well-founded techniques to choose from. However, many modelling problems suggest a formulation employing multiple penalties, and here general methodology is lacking. A wide family of models with multiple penalties can be fitted to data by iterative solution of the generalized ridge regression problem minimize ||W 1/2 (Xp−y) ||2ρ+Σi=1 m θi p′S i p (p is a parameter vector, X a design matrix, S i a non-negative definite coefficient matrix defining the ith penalty with associated smoothing parameter θi, W a diagonal weight matrix, y a vector of data or pseudodata and ρ an ‘overall’ smoothing parameter included for computational efficiency). This paper shows how smoothing parameter selection can be performed efficiently by applying generalized cross-validation to this problem and how this allows non-linear, generalized linear and linear models to be fitted using multiple penalties, substantially increasing the scope of penalized modelling methods. Examples of non-linear modelling, generalized additive modelling and anisotropic smoothing are given.

'They might as well brand us': working-class resistance to compulsory vaccination in Victorian England.

From its origins in resistance to the 1853 Compulsory Vaccination Act, the Victorian anti-vaccination movement successfully challenged the public health policies of an increasingly interventionist state. Anti-vaccinationists were not only middle-class reformers, but were also drawn from a politically active working class. These campaigners saw compulsory vaccination as an extreme example of class legislation, for its policy and administration implicitly targeted working-class infants and inflicted multiple penalties on a public who considered themselves 'conscientious objectors'. Anti-vaccinationism was quickly absorbed into English working-class culture. Indeed, it helped to reorganize working-class identities around the site of the vulnerable body thereby absorbing many people into a working class who interpreted the violation of their bodies as a form of political tyranny. Participation in this movement was, however, also an exclusive exercise for anti-vaccinators, as respectable working-class citizens distinguished themselves from members of the 'undeserving' classes. This paper explores the class nature of the Vaccination Acts, their relationship to the New Poor Law, and the political implications of their administration. It also imbeds anti-vaccinationism firmly within working-class culture, illustrating the campaign's relationship to popular protest and entertainment, and this legislation's impact upon working-class bodies.

The Need for a National Maternity Policy in the USA

In the age of working mothers, the human resources base of society cannot go unprotected. An established labour market trend indicates the prompt return of female workers to their jobs following the birth of their babies. That fact appears to be not so widely recognised by policy makers and seems to be deliberately ignored by the business community. American society, legislatively speaking, is still operating on the basis of an old norm — that of women dropping out of the labour force to give birth to children and to raise a family. This perception is not valid and should no longer be held. “Working mothers” represent a universal pattern in the industrial countries of the world. This is evidenced by the high labour force participation rate (80 per cent) of young women of child‐bearing age (25–39 years). There is a worldwide general consensus on the effective need to protect working mothers against enduring multiple penalties in meeting work and family obligations. These sacrifices include loss of income and job, physical and psychological strain and stress as well as infant development risks. The ILO Maternity and Protection resolution of 1919 and its subsequent revisions called for supportive pregnancy and maternity measures. All the industrial and developing countries — except five — have whole‐heartedly adopted and even exceeded the ILO guidelines. The US, to this date, has not ratified the ILO recommendations, and has no national maternity policy despite a high and increasing percentage of young female workers in the labour market. Instead, there is strong opposition to any move in this direction.

THE EFFECT OF TAX LAWS AND TAX ADMINISTRATION ON TAX COMPLIANCE: THE CASE OF THE U.S. INDIVIDUAL INCOME TAX

We develop and estimate an economic model of tax compliance which specifically incorporates important aspects of the U.S. Federal income tax laws (e. g., a progressive tax structure and multiple penalties for tax non-compliance each of which depend on the amount of taxes not paid). On the one hand, our model suggests that increases in the probability of audit and increases in such things as information reporting and tax withholding will be likely to increase compliance. On the other, it suggests that increasing moral ambivalence toward tax compliance will increase non-compliance. Our empirical results, in general, support our theoretical model and indicate that IRS compliance activities, taxpayer opportunities for non-compliance and taxpayer attitudes all have significant effects on compliance. Possibly our most interesting empirical finding is that the decline in audit rates during the 1970s may have accounted for a substantial portion of the decline in compliance during that period.

Multi-parameter Tikhonov regularization

We study multi-parameter Tikhonov regularization, i.e., with multiple penalties. Such models are useful when the sought-for solution exhibits several distinct features simultaneously. Two choice rules, i.e., discrepancy principle and balancing principle, are studied for choosing an appropriate (vector-valued) regularization parameter, and some theoretical results are presented. In particular, the consistency of the discrepancy principle as well as convergence rate are established, and an a posteriori error estimate for the balancing principle is established. Also two fixed point algorithms are proposed for computing the regularization parameter by the latter rule. Numerical results for several nonsmooth multi-parameter models are presented, which show clearly their superior performance over their single-parameter counterparts.